VLIW Code Generation for a Convolutional Network Accelerator

Maurice Peemen, Wisnu Pramadi, Bart Mesman, Henk Corporaal
2015 Proceedings of the 18th International Workshop on Software and Compilers for Embedded Systems - SCOPES '15  
This paper presents a compiler flow to map Deep Convolutional Networks (ConvNets) to a highly specialized VLIW accelerator core targeting the low-power embedded market. Earlier works have focused on energy efficient accelerators for this class of algorithms, but none of them provides a complete and practical programming model. Due to the large parameter set of a ConvNet it is essential that the user can abstract from the accelerator architecture and does not have to rely on an error prone and
more » ... -hoc assembly programming model. By using modulo scheduling for software pipelining we demonstrate that our automatic generated code achieves equal or within 5-20% less hardware utilization w.r.t. code written manually by experts. Our compiler removes the huge manual workload to efficiently map ConvNets to an energy-efficient core for the next-generation mobile and wearable devices.
doi:10.1145/2764967.2771928 dblp:conf/scopes/PeemenPMC15 fatcat:xbrlsv3bbfd3te4c7rrc3a7haq